Systems and methods for detecting changes in energy usage in a building

ABSTRACT

A computer system for use with a building management system for a building includes a processing circuit configured to automatically identify a change in a building&#39;s energy usage model based on data received from the building management system. The processing circuit may be configured to communicate the identified change in the static factor to at least one of (a) a module for alerting a user to the identified change and (b) a module for initiating an adjustment to the energy model for a building in response to the identified change.

CROSS-REFERENCES TO RELATED PATENT APPLICATIONS

This application is a continuation of U.S. application Ser. No.13/167,571, filed Jun. 23, 2011, which is a continuation-in-part of U.S.application Ser. No. 13/023,392, filed Feb. 8, 2011, which is acontinuation-in-part of, U.S. application Ser. No. 12/819,977, filedJun. 21, 2010, which claims the benefit of U.S. Provisional ApplicationNo. 61/219,326, filed Jun. 22, 2009, U.S. Provisional Application No.61/234,217, filed Aug. 14, 2009, and U.S. Provisional Application No.61/302,854, filed Feb. 9, 2010. The entireties of U.S. application Ser.Nos. 13/167,571, 13/023,392, 12/819,977, 61/219,326, 61/234,217, and61/302,854 are incorporated by reference herein.

BACKGROUND

The present disclosure generally relates to energy conservation in abuilding. The present disclosure relates more specifically to detectingchanges in the energy use of a building.

In many areas of the country, electrical generation and transmissionassets have or are reaching full capacity. One of the most costeffective ways to ensure reliable power delivery is to reduce demand(MW) by reducing energy consumption (MWh). Because commercial buildingsconsume a good portion of the generated electricity in the UnitedStates, a major strategy for solving energy grid problems is toimplement energy conservation measures (ECMs) within buildings. Further,companies that purchase energy are working to reduce their energy costsby implementing ECMs within buildings.

Entities that invest in ECMs typically want to verify that the expectedenergy savings associated with ECMs are actually realized (e.g., forverifying the accuracy of return-on-investment calculations). Federal,state, or utility based incentives may also be offered to encourageimplementation of ECMs. These programs will have verificationrequirements. Further, some contracts between ECM providers (e.g., acompany selling energy-efficient equipment) and ECM purchasers (e.g., abusiness seeking lower ongoing energy costs) establish relationshipswhereby the ECM provider is financially responsible for energy or costsavings shortfalls after purchase of the ECM. Accordingly, Applicantshave identified a need for systems and methods for measuring andverifying energy savings and peak demand reductions in buildings.Applicants have further identified a need for systems and methods thatautomatically measure and verify energy savings and peak demandreductions in buildings.

Once a baseline model has been developed for measuring and verifyingenergy savings in a building, the baseline model may be used to estimatethe amount of energy that would have been used by the building if ECMswere not performed. Calculation of the energy avoidance may be rathersimple if the baseline model is accurate and routine adjustments relatedto building usage are included in the baseline model. However,non-routine adjustments related to building usage may not be included inthe baseline model, and therefore not included in the energy avoidancecalculation. Examples of static factors for which non-routineadjustments may occur include the size of the facility, the hours ofoperation of the building, the number of employees using the building,the number of computer servers in the building, etc.

Such non-routine adjustments may cause problems during the reportingperiod when trying to verify energy savings in a building. An unnoticedchange (e.g., a non-routing adjustment) in static factors may causeenergy savings in the building to be underestimated. This may cause anengineer to miss requirements in a contract or cause a building ownernot to invest money in ECMs even though the actual return on investmentwas high.

Unaccounted for changes in static factors that occur during the baselineperiod may also negatively impact calculations (e.g., creation of anaccurate baseline model). If changes in static factors occur during thebaseline period, the statistical regression module used to build thebaseline model may essentially be based on a midpoint between energyusage before the change in static factors and energy usage after thechange in static factors. Such a situation may lead to an inaccurateestimation of energy usage. For example, if a change in static factorscauses a decrease in energy usage, the energy savings of the buildingdue to the ECMs will be overestimated by the baseline model, and thebuilding owner may be disappointed with a resulting return oninvestment. As another example, if a change in static factors causes anincrease in energy usage, the energy savings of the building will beunderestimated by the baseline model, and a performance contractor maymiss the contractual guarantee of energy savings and be required toreimburse the building owner. Monitoring static factors traditionallyrequires manual monitoring. This may lead to changes in static factorsgoing unnoticed or incorrectly calculated.

SUMMARY

One embodiment of the invention relates to a computer system for usewith a building management system for a building includes a processingcircuit configured to automatically identify a change in a building'senergy usage model based on data received from the building managementsystem. The identified change in the building's energy usage model maybe a static factor change. The processing circuit may be configured tocommunicate the identified change in the static factor to at least oneof (a) a module for alerting a user to the identified change and (b) amodule for initiating an adjustment to the energy model for a buildingin response to the identified change.

Another embodiment of the invention relates to a method for identifyingchanges that affect a building's energy usage during a baseline periodbeing used to generate a baseline energy usage model. The methodincludes calculating energy usage models for varying windows of timewithin the baseline period. The method further includes comparingcoefficients of the calculated energy usage models for varying windowsof time. The method further includes outputting an indication that thechange has occurred based on the comparison.

Yet another embodiment of the invention relates to a method foridentifying changes that affect a building's energy usage during areporting period after an energy conservation measure has been appliedto the building. The method includes calculating a first energy usagemodel for a first portion of the reporting period. The method furtherincludes calculating a second energy usage model a second portion of thereporting period. The method further includes determining whether achange that affects the building's energy usage has occurred by oncomparing the model coefficients as associated with the first energyusage model to the model coefficients as associated with the secondenergy usage model. The method further includes outputting the result ofthe determination to a building management system component.

Yet another embodiment of the invention relates to computer-readablemedia with computer-executable instructions embodied thereon that whenexecuted by a computer system perform a method for use with a buildingmanagement system in a building. The instructions include instructionsfor using historical data from the building management system to selecta set of variables estimated to be significant to energy usage in thebuilding. The instructions further include instructions for applying aregression analysis to the selected set of variables to generate abaseline model for predicting energy usage in the building. Theinstructions further include instructions for monitoring data of thebuilding management system after installation of energy conservationmeasures to determine whether the baseline model can accurately be usedto estimate the energy that would have been used by the building priorto the installation of energy conservation measures. The instructionsfurther include instructions for outputting at least one of: (a) anindication that an energy use factor of the building has changed, and(b) an indication that the baseline model should be adjusted.

Alternative exemplary embodiments relate to other features andcombinations of features as may be generally recited in the claims.

BRIEF DESCRIPTION OF THE FIGURES

The disclosure will become more fully understood from the followingdetailed description, taken in conjunction with the accompanyingfigures, wherein like reference numerals refer to like elements, inwhich:

FIG. 1A is a flow chart of a process for measuring and verifying energysavings and peak demand reductions in a building, according to anexemplary embodiment;

FIG. 1B is a simplified block diagram of a system for completing orfacilitating the process of FIG. 1A, according to an exemplaryembodiment;

FIG. 1C is a block diagram of a system for measuring and verifyingenergy savings in a building is shown, according to an exemplaryembodiment;

FIG. 2 is a detailed block diagram of the baseline calculation module ofFIG. 1C, according to an exemplary embodiment;

FIG. 3A is a flow chart of a process for selecting observed variabledata to use for generation of the baseline model, according to anexemplary embodiment;

FIG. 3B is a flow chart of a process for selecting calculated variabledata to use for generation of the baseline model, according to anexemplary embodiment;

FIGS. 4A-4E are more detailed flow charts of the process of FIG. 3B,according to an exemplary embodiment;

FIG. 5 is a flow chart of the objective function used in the goldensection search of the process of FIGS. 4A-E shown in greater detail,according to an exemplary embodiment;

FIG. 6 is a flow chart of a process of calculating enthalpy, accordingto an exemplary embodiment;

FIG. 7 is a flow chart of a process for identifying changes in thestatic factors of a baseline model such as that discussed with referenceto previous Figures, according to an exemplary embodiment;

FIG. 8A is a flow chart of a process for detecting changes in staticfactors of a baseline model during a baseline period, according to anexemplary embodiment;

FIG. 8B is a flow chart of a process for calculating an observedstatistic for detecting changes in static factors during a baselineperiod, according to an exemplary embodiment;

FIG. 9A is a flow chart of a process for detecting changes in staticfactors of a baseline model during a reporting period, according to anexemplary embodiment;

FIG. 9B is a flow chart of a process for calculating an observedstatistic for detecting changes in static factors during a reportingperiod, according to an exemplary embodiment;

FIG. 10 is a detailed block diagram of a baseline calculation module fordetecting changes during a baseline period, according to an exemplaryembodiment;

FIG. 11 is a detailed block diagram of a facility monitoring module fordetecting changes during a reporting period, according to an exemplaryembodiment;

FIG. 12 is a flow chart of evaluating an inverse of a cumulativeprobability distribution function for determining a critical value forthe process of FIG. 9A, according to an exemplary embodiment; and

FIG. 13 is a flow chart of calculating a P-value for the process of FIG.8A, the P-value for use in determining changes in static factors,according to an exemplary embodiment.

DESCRIPTION

Before turning to the figures, which illustrate the exemplaryembodiments in detail, it should be understood that the disclosure isnot limited to the details or methodology set forth in the descriptionor illustrated in the figures. It should also be understood that theterminology is for the purpose of description only and should not beregarded as limiting.

Referring generally to the Figures, a computer system for use with abuilding management system in a building is provided for automaticallydetecting changes in factors correlated to energy use that are expectedto remain static (e.g., one or more static factors). The computer systemincludes a processing circuit configured to automatically identify achange in such a static factor of a building based on data received fromthe building management system. The processing circuit may be configuredto communicate the identified change in the static factor to at leastone of (a) a module for alerting a user to changes and (b) a module forinitiating adjustment to an energy model for a building.

Embodiments of the present disclosure are configured to automatically(e.g., via a computerized process) calculate a baseline model (i.e., apredictive model) for use in measuring and verifying energy savings andpeak demand reductions attributed to the implementation of energyconservation measures in building. The calculation of the baseline modelmay occur by applying a partial least squares regression (PLSR) methodto data from a building management system (BMS). The baseline model isused to predict energy consumption and peak demand reductions in abuilding if an ECM were not installed or used in the building. Actualenergy consumption using the ECM is subtracted from the predicted energyconsumption to obtain an energy savings estimate or peak demandestimate.

The computerized process can utilize many collinear or highly correlateddata points from the BMS to calculate the baseline model using the PLSRalgorithm. Data clean-up, data synchronization, and regression analysisactivities of the computerized process can be used to prepare the dataand to tune the baseline model for improved performance relative to thepertinent data. Further, baseline contractual agreements and violationsbased on the generated baseline model may be determined with apredetermined statistical confidence.

To provide improved performance over conventional approaches to baselinecalculations, an exemplary embodiment includes the following features:one or more computerized modules for automatically identifying whichpredictor variables (e.g., controllable, uncontrollable, etc.) are themost important to an energy consumption prediction and a computerizedmodule configured to automatically determine the baseline model usingthe identified predictor variables determined to be the most importantto the energy consumption prediction.

While the embodiments shown in the figures mostly relate to measuringand verifying energy consumption and energy savings in a building withthe use of expected values as inputs, it should be understood that thesystems and methods below may be used to measure and verify peak demandconsumption and savings with the use of maximum values as inputs.

Measuring and Verifying Energy Savings in Buildings

Referring now to FIGS. 1A and 1B, a process 100 for measuring andverifying energy savings and peak demand in a building is shown,according to an exemplary embodiment. Process 100 is shown to includeretrieving historical building and building environment data 120 from apre-retrofit period (step 102). Input variables retrieved in step 102and used in subsequent steps may include both controllable variables(i.e., variables that may be controlled by a user such as occupancy ofan area and space usage) and uncontrollable variables (e.g., outdoortemperature, solar intensity and duration, humidity, other weatheroccurrences, etc.).

Process 100 further includes using the data obtained in step 102 tocalculate and select a set of variables significant to energy usage inthe building (step 104). Step 104 may include calculating variables thatmay be used to determine energy usage in the building. For example,calculated variables such as cooling degree days, heating degree days,cooling energy days, or heating energy days that are representative ofenergy usage in the building relating to an outside air temperature andhumidity may be calculated. Energy days (cooling energy days and heatingenergy days) are herein defined as a predictor variable that combinesboth outside air temperature and outside air humidity. Energy daysdiffer from degree days at least in that the underlying integration isbased on the calculated outside air enthalpy. Step 104 may include theselection of a set of calculated variables, variables based on a dataset from the data received in step 102, or a combination of both. Forexample, the set of variables may include variables associated with adata set of the building (e.g., occupancy and space usage of an area,outdoor air temperature, humidity, solar intensity) and calculatedvariables (e.g., occupancy hours, degree days, energy days, etc.).Variables and data that are not significant (e.g., that do not have animpact on energy usage in the building) may be discarded or ignored byprocess 100.

The set of variables is then used to create a baseline model 126 thatallows energy usage or power consumption to be predicted (step 106).With reference to the block diagram of FIG. 1B, baseline model 126 maybe calculated using a baseline model generator 122 (e.g., a computerizedimplementation of a PLSR algorithm).

Process 100 further includes storing agreed-upon ranges of controllableinput variables and other agreement terms in memory (step 108). Thesestored and agreed-upon ranges or terms are used as baseline modelassumptions in some embodiments. In other embodiments the baseline modelor a resultant contract outcome may be shifted or changed whenagreed-upon terms are not met.

Process 100 further includes conducting an energy efficient retrofit ofbuilding equipment (step 110). The energy efficient retrofit may includeany one or more process or equipment changes or upgrades expected toresult in reduced energy consumption by a building. For example, anenergy efficient air handling unit having a self-optimizing controllermay be installed in a building in place of a legacy air handling unitwith a conventional controller.

Once the energy efficient retrofit is installed, process 100 beginsobtaining measured energy consumption 130 for the building (step 112).The post-retrofit energy consumption 130 may be measured by a utilityprovider (e.g., power company), a system or device configured tocalculate energy expended by the building HVAC system, or otherwise.

Process 100 further includes applying actual input variables 124 of thepost-retrofit period to the previously created baseline model 126 topredict energy usage of the old system during the post-retrofit period(step 114). This step results in obtaining a baseline energy consumption128 (e.g., in kWh) against which actual energy consumption 130 from theretrofit can be compared.

In an exemplary embodiment of process 100, estimated baseline energyconsumption 128 is compared to measured energy consumption 130 bysubtracting measured energy consumption 130 during the post-retrofitperiod from estimated baseline energy consumption 128 (step 116). Thissubtraction will yield the energy savings 132 resulting from theretrofit. The energy savings 132 resulting from the retrofit ismultiplied or otherwise applied to utility rate information for theretrofit period to monetize the savings (step 118). Steps 114 and 116may further include determining a peak demand reduction in the buildingand monetizing cost related to the reduction.

Referring now to FIG. 1C, a more detailed block diagram of a BMScomputer system 200 for measuring and verifying energy savings in abuilding is shown, according to an exemplary embodiment. System 200includes multiple inputs 202 from disparate BMS sources. Inputs 202 arereceived and parsed or otherwise negotiated by an information aggregator204 of the processing circuit 254.

BMS computer system 200 includes a processing circuit 250 including aprocessor 252 and memory 254. Processor 252 can be implemented as ageneral purpose processor, an application specific integrated circuit(ASIC), one or more field programmable gate arrays (FPGAs), a group ofprocessing components, or other suitable electronic processingcomponents. Memory 254 is one or more devices (e.g., RAM, ROM, Flashmemory, hard disk storage, etc.) for storing data and/or computer codefor completing and/or facilitating the various processes, layers, andmodules described in the present disclosure. Memory 254 may be orinclude volatile memory or non-volatile memory. Memory 254 may includedatabase components, object code components, script components, or anyother type of information structure for supporting the variousactivities and information structures described in the presentdisclosure. According to an exemplary embodiment, memory 254 iscommunicably connected to processor 252 via processing circuit 250 andincludes computer code for executing (e.g., by processing circuit 250and/or processor 252) one or more processes described herein.

Memory 254 includes information aggregator 204. Information aggregator204 may serve as middleware configured to normalize communications ordata received from the multiple inputs. Information aggregator 204 maybe a middleware appliance or module sold by Johnson Controls, Inc.Information aggregator 204 is configured to add data to a BMS database206. A data retriever 208 is configured to retrieve (e.g., query) datafrom BMS database 206 and to pass the retrieved data to baselinecalculation module 210.

Baseline calculation module 210 is configured to create a baseline modelusing historical data from the multiple inputs and aggregated in BMSdatabase 206 or other data sources. Some of the information may bereceived from sources other than building data sources (e.g., weatherdatabases, utility company databases, etc.). The accuracy of thebaseline model will be dependent upon errors in the data received.

Baseline calculation module 210 is shown to include data clean-up module212. Data clean-up module 212 receives data from data retriever 208 andprefilters the data (e.g., data scrubbing) to discard or format baddata. Data clean-up module 212 conducts one or more checks to determinewhether the data is reliable, whether the data is in the correct format,whether the data is or includes a statistical outlier, whether the datais distorted or “not a number” (NaN), whether the sensor orcommunication channel for a set of data has become stuck at some value,and if the data should be discarded. Data clean-up module 212 may beconfigured to detect errors via, for example, threshold checks orcluster analysis.

Baseline calculation module 210 is further shown to include datasynchronization module 214. Data synchronization module 214 receives thedata after the data is “cleaned up” by data clean-up module 212 and isconfigured to determine a set of variables for use in generating thebaseline model. The variables may be calculated by module 214, may bebased on received data from data retriever 208 and data clean-up module212, or a combination of both. For example, data synchronization module214 may determine variables (e.g., cooling and heating degree days andcooling and heating energy days) that serve as a proxy for energy usageneeded to heat or cool an area of the building. Data synchronizationmodule 214 may then further determine which type of calculated variableto use (e.g., whether to use degree days or energy days in theregression analysis that generates the baseline model). Further, datasynchronization module 214 may identify and use measured data receivedfrom data retriever 208 and formatted by data clean-up module for use inthe set of variables. For example, module 214 may select temperaturedata received from data retriever 208 as a predictor variable for energyusage in the building.

Baseline calculation module 210 further includes regression analysismodule 216. Regression analysis module 216 is configured to generate thebaseline model based on the set of variables from data synchronizationmodule 214. According to one exemplary embodiment, a partial leastsquares regression (PLSR) method may be used to generate the baselinemodel. According to other embodiments, other regression methods (e.g., aprincipal component regression (PCR), ridge regression (RR), ordinaryleast squares regression (OLSR)) are also or alternatively used in thebaseline model calculation. The PLSR method is based on a lineartransformation from the set of variables from module 214 to a linearmodel that is optimized in terms of predictivity.

Baseline calculation module 210 further includes cross-validation module218. Cross-validation module 218 is configured to validate the baselinemodel generated by regression analysis module 216. Validation of thebaseline model may include ensuring there is no overfitting of thebaseline model (e.g., having too many variables or inputs influencingthe model), determining a correct order or number of components in themodel, or conducting other tests or checks of the baseline module outputby regression analysis module 216. Baseline calculation module 210 andsub-modules 212-218 are shown in greater detail in FIG. 2 and subsequentfigures.

Post retrofit variable data 222 is applied to baseline model 220generated by baseline calculation module 210 (e.g., data relating toestimated energy use of the building) to obtain a resultant baselineenergy consumption. Measured energy consumption 224 from the building issubtracted from the resultant baseline energy consumption at element 225to obtain energy savings data 226. Energy savings data 226 may be usedto determine payments (e.g., from the retrofit purchaser to the retrofitseller), to demonstrate the new equipment's compliance with a guaranteedlevel of performance, or as part of a demand-response commitment or bidvalidation. Energy savings data 226 may relate to overall energyconsumption and/or peak demand reduction.

Energy savings data 226 or other information from the prior calculationsof the system is used to monitor a building after retrofit to ensurethat the facility occupants have not violated the terms of the baselineagreement (e.g., by substantially adding occupants, by changing thebuilding space use, by bringing in more energy using devices, bysubstantially changing a setpoint or other control setting, etc.).Conventionally this involves making periodic visits to the facilities,reviewing job data, and/or making specialized measurements. Becausevisits and specialized reviews are time consuming, they are often notdone, which puts any savings calculations for a period of time inquestion.

System 200 includes an Exponentially Weighted Moving Average (EWMA)control module 228 configured to automate a baseline term validationprocess. EWMA control module 228 monitors the difference between thepredicted and measured consumption. Specifically, EWMA control module228 checks for differences between the predicted and measuredconsumption that are outside of predetermined statistical probabilitythresholds and provides the results to a facility monitoring module 230.Any statistically unlikely occurrences can cause a check of related dataagainst baseline agreement information 232, used to update baselineagreement information, or are provided to a user output/system feedbackmodule 234. User output/system feedback module 234 may communicate alarmevents to a user in the form of a displayed (e.g., on an electronicdisplay) EWMA chart configured to highlight unexpected shifts in thecalculated energy savings. Input calibration module 236 may receivefeedback from module 234 and additionally provide data to data retriever208 regarding instructions to add or remove variables from considerationfor the baseline model in the future. In other embodiments, different oradditional control modules may implement or include statistical processcontrol approaches other than or in addition to EWMA to provide baselinevalidation features.

BMS computer system 200 further includes a user input/output (I/O) 240.User I/O 240 is configured to receive a user input relating to the dataset used by baseline calculation module 210 and other modules of system200. For example, user I/O 240 may allow a user to input data intosystem 200, edit existing data points, etc. System 200 further includescommunications interface 242. Communications interface 242 can be orinclude wired or wireless interfaces (e.g., jacks, antennas,transmitters, receivers, transceivers, wire terminals, etc.) forconducting data communications with the BMS, subsystems of the BMS, orother external sources via a direct connection or a network connection(e.g., an Internet connection, a LAN, WAN, or WLAN connection, etc.).

Referring now to FIG. 2, baseline calculation module 210 is shown ingreater detail, according to an exemplary embodiment. Baselinecalculation module 210 includes data clean-up module 212. Data clean-upmodule 212 generally receives data from the BMS computer system of thebuilding and pre-filters the data for data synchronization module 214and the other modules of baseline calculation module 210. Data clean-upmodule 212 includes outlier analysis module 256, data formatting module258, and sorting module 260 for pre-filtering the data. Data clean-upmodule 212 uses sub-modules 256-260 to discard or format bad data bynormalizing any formatting inconsistencies with the data, removingstatistical outliers, or otherwise preparing the data for furtherprocessing. Data formatting module 258 is configured to ensure that likedata is in the same correct format (e.g., all time-based variables arein the same terms of hours, days, minutes, etc.). Sorting module 260 isconfigured to sort data for further analysis (e.g., place inchronological order, etc.).

Outlier analysis module 256 is configured to test data points anddetermine if a data point is reliable. For example, if a data point ismore than a threshold (e.g., three standard deviations, four standarddeviations, or another set value) away from the an expected value (e.g.,the mean) of all of the data points, the data point may be determined asunreliable and discarded. Outlier analysis module 256 may furthercalculate the expected value of the data points that each data point isto be tested against. Outlier analysis module 256 may be configured toreplace the discarded data points in the data set with a NaN or anotherflag such that the new value will be skipped in further data analysis.

According to another exemplary embodiment, outlier analysis module 256can be configured to conduct a cluster analysis. The cluster analysismay be used to help identify and remove unreliable data points. Forexample, a cluster analysis may identify or group operating states ofequipment (e.g., identifying the group of equipment that is off). Acluster analysis can return clusters and centroid values for the groupedor identified equipment or states. The centroid values can be associatedwith data that is desirable to keep rather than discard. Clusteranalyses can be used to further automate the data clean-up processbecause little to no configuration is required relative to thresholding.

Data clean-up module 212 may further include any other pre-filteringtasks for sorting and formatting the data for use by baselinecalculation module 210. For example, data clean-up module 212 mayinclude an integrator or averager which may be configured to smoothnoisy data (e.g., a varying number of occupants in a building area). Theintegrator or averager may be used to smooth data over a desiredinterval (e.g., a 15 minute average, hourly average, etc.).

Baseline calculation module 210 includes data synchronization module214. Data synchronization module 214 is configured to select a possibleset of variables estimated to be significant to energy usage in thebuilding. Data synchronization module 214 selects the possible set ofvariables (e.g., a preliminary set of variables) that are provided tostepwise regression module 284 for selection of the actual set ofvariables to use to generate the baseline model. According to variousexemplary embodiments, the selection of some or all of the set ofvariables to use for baseline model generation may occur in datasynchronization module 214, stepwise regression analysis 284, or acombination of both. Data synchronization module 214 includessub-modules for calculating predictor variables and selecting one ormore of the predicted variables to include in the possible set ofvariables. Data synchronization module 214 further includes sub-modulesfor selecting observed (e.g., measured) data points for the set ofvariables.

According to one exemplary embodiment, data synchronization module 214is configured to calculate degree days and energy days (e.g., apredictor variable associated with heating or cooling of a building) anddetermine which of these predictors should be used to yield a betterbaseline model. The outputs of data synchronization module 214 (e.g.,inputs provided to regression analysis module 216) may include themeasurements or predictor variables to use, a period of time associatedwith the measurements or predictor variables, and errors associated withthe data included in the measurements or predictor variables.

Data synchronization module 214 includes enthalpy module 262, balancepoint module 264, model determination module 266, regression periodmodule 268, integration module 270, NaN module 272, missing days module274, workdays module 276, and observed variable selection module 278.Enthalpy module 262 is configured to calculate an enthalpy given atemperature variable and a humidity variable. Enthalpy module 262combines an outdoor temperature variable and an outside air humidityvariable via a nonlinear transformation or another mathematical functioninto a single variable. The single variable may then be used by baselinecalculation module 210 as a better predictor of a building's energy usethan using both temperature and humidity values separately.

Balance point module 264 is configured to find an optimal balance pointfor a calculated variable (e.g., a variable based on an enthalpy valuecalculated in enthalpy module 262, an outdoor air temperature variable,etc.). Balance point module 264 determines a base value for the variablefor which the estimated variance of the regression errors is minimized.Model determination module 266 is configured to determine a type ofbaseline model to use for measuring and verifying energy savings. Thedetermination may be made based on an optimal balance point generated bybalance point module 264. Modules 264, 266 are described in greaterdetail in FIGS. 4A-4E.

Regression period module 268 is configured to determine periods of timethat can be reliably used for model regression by baseline calculationmodule 210 and data synchronization module 214. Regression period module268 may identify period start dates and end dates associated withcalculated and measured variables for the data synchronization.Regression period module 268 may determine the start date and end datecorresponding with the variable with the longest time interval (e.g.,the variable for which the most data is available). For example,regression period module 268 determines the period by finding the periodof time which is covered by all variables, and providing the start dateand end date of the intersection to data synchronization module 214.Regression period module 268 is further configured to identify datawithin the periods that may be erroneous or cannot be properlysynchronized.

Integration module 270 is configured to perform an integration over avariable structure from a given start and end time period (e.g., a timeperiod from regression period module 268). According to an exemplaryembodiment, integration module 270 uses a trapezoidal method ofintegration. Integration module 270 may receive an input from balancepoint module 264 or another module of data synchronization module 214for performing an integration for a balance point determined by balancepoint module 264. NaN module 272 is configured to identify NaN flags ina variable structure. NaN module 272 is further configured to replacethe NaN flags in the variable structure via interpolation. NaN module272 may receive an input from, for example, data clean-up module 212,and may be configured to convert the outlier variables and NaNsdetermined in module 212 into usable data points via interpolation.

Missing days module 274 is configured to determine days for which isthere is not enough data for proper integration performance. Missingdays module 274 compares the amount of data for a variable for a givenday (or other period of time) and compares the amount to a threshold(e.g., a fraction of a day) to make sure there is enough data toaccurately calculate the integral. Workdays module 276 is configured todetermine the number of work days in a given interval based on the startdate and end date of the interval. For example, for a given start dateand end date, workdays module 276 can determine weekend days andholidays that should not figure into the count of number of work days ina given interval. Modules 274, 276 may be used by data synchronizationmodule 214 to, for example, identify the number of days within a timeinterval for which there exists sufficient data, identify days for whichdata should not be included in the calculation of the baseline model,etc.

Observed variable selection module 278 is configured to receive observedor measured data from the BMS and determine which observed data shouldbe used for baseline model generation based on the selection ofcalculated data in modules 264-266. For example, when balance pointmodule 264 determines a calculated variable, observed variable selectionmodule 278 is configured to determine if there is enough predictorvariable data for the observed variable. According to an exemplaryembodiment, the predictor variable data and observed variable data for aspecific variable (e.g., temperature) may only be used when sufficientpredictor variable data (e.g., degree days) for the observed variabledata exists. For example, if the predictor variable data is availableover a specified range (e.g., 20 days, 2 months, or any other length oftime), then module 278 may determine there is enough predictor variabledata such that the predictor variable data and observed variable datacan be used for baseline model generation. Observed variable selectionmodule 278 is described in greater detail in FIG. 3A.

Baseline calculation module 210 further includes regression analysismodule 216. Regression analysis module 216 is configured to generate thebaseline model via a PLSR method. Regression analysis module 216includes baseline model generation module 280 for generating thebaseline model and PLSR module 282 for receiving data from datasynchronization module 214, applying the data to a PLSR method for, andproviding baseline model generation module 280 with the method output.

Baseline model generation module 280 is configured to generate thebaseline model. Baseline model generation module 280 is configured touse PLSR module 282 to perform PLSR of the data and stepwise regressionmodule 284 to determine the predictor variables for the baseline modeland to eliminate insignificant variables. Module 280 is configured toprovide, as an output, the baseline model along with calculating variousstatistics for further analysis of the baseline model (e.g., computingthe number of independent observations of data in the data set used,computing the uncertainty of the model, etc.).

Regression analysis module 216 is further shown to include stepwiseregression module 284. Stepwise regression module 284 is configured toperform stepwise linear regression in order to eliminate statisticallyinsignificant predictor variables from an initial set of variablesselected by data synchronization module 214. In other words, stepwiseregression module 284 uses stepwise regression to add or removepredictor variables from a data set (e.g., the data set from datasynchronization module 214) for further analysis.

A stepwise regression algorithm of module 284 is configured to add orremove predictor variables from a set for further analysis in asystematic way. At each step the algorithm conducts statisticalhypothesis testing (e.g., by computing a probability of obtaining a teststatistic, otherwise known as a p-value, of an F-statistic, which isused to describe the similarity between data values) to determine if thevariable should be added or removed. For example, for a particularvariable, if the variable would have a zero (or near zero) coefficientif it were in the baseline model, then the variable is removed fromconsideration for the baseline model. According to various alternativeembodiments, other approaches to stepwise regression are used (e.g.,factorial designs, principal component analysis, etc.). Referring alsoto FIG. 1C, instructions to add or remove variables from futureconsideration based on the analysis of module 216 may be provided to,for example, input calibration module 236 for affecting the queries runby data retriever 208.

PLSR module 282 is configured to receive a subset of the variables fromdata synchronization module 214 which has been selected by stepwiseregression module 284, and to compute a partial least squares regressionof the variables in order to generate a baseline model. According tovarious alternative embodiments, other methods (e.g., a principalcomponent regression (PCR), ridge regression (RR), ordinary leastsquares regression (OLSR)) are also or alternatively used in thebaseline model calculation instead of a PLSR method.

Baseline models calculated using historical data generally include fourpossible sources of error: modeling errors, sampling errors, measurementerrors, and errors relating to multiple distributions in the data set.Sampling errors occur when the number of data samples used is too smallor otherwise biased. Measurement errors occur when there is sensor orequipment inaccuracy, due to physics, poor calibration, a lack ofprecision, etc. Modeling errors (e.g., errors associated with the dataset) occur due to inaccuracies and inadequacies of the algorithm used togenerate the model. Errors relating to multiple distributions in thedata set occur as more data is obtained over time. For example, over aone to three year period, data may be collected for the period and olderdata may become obsolete as conditions change. The older data maynegatively impact the prediction capabilities of the current baselinemodel.

Conventional baseline energy calculations use ordinary least squaresregression (OLS). For example, ASHRAE Guideline 14-2002 titled“Measurement of Energy Demand Savings” and “The InternationalPerformance Measurement and Verification Protocol” (IPMVP) teach thatOLS should be used for baseline energy calculations. For OLS:y=1*β₀ +Xβ _(OLS)+εwhere y is a vector of the response variables, X is a matrix consistingof n observations of the predictor variables, β₀ is an unknown constant,β_(OLS) is an unknown vector of OLS regression coefficients, and ε is avector of independent normally distributed errors with zero mean andvariance σ². The regression coefficients are determined by solving thefollowing equation:β_(OLS)=(X ^(T) X)⁻¹ X ^(T) y.

PLSR may outperform OLS in a building environment where the inputs to anenergy consumption can be many, highly correlated, or collinear. Forexample, OLS can be numerically unstable in such an environmentresulting in large coefficient variances. This occurs when X^(T)X, whichis needed for calculating OLS regression coefficients, becomesill-conditioned in environments where the inputs are many and highlycorrelated or collinear. In alternative embodiments, PCR or RR are usedinstead of or in addition to PLSR to generate a baseline model. In thepreferred embodiment PLSR was chosen due to its amenability toautomation, its feature of providing lower mean square error (MSE)values with fewer components than methods such as PCR, its feature ofresolving multicollinearity problems attributed to methods such as OLS,and due to its feature of using variance in both predictor and responsevariables to construct model components.

Baseline calculation module 210 is further shown to includecross-validation module 218. Cross-validation module 218 is configuredto validate the baseline model generated by regression analysis module216 (e.g., there is no overfitting of the model, the order and number ofvariables in the model is correct, etc.) by applying data for a testperiod of time (in the past) to the model and determining whether themodel provides a good estimate of energy usage. Cross-validation of thebaseline model is used to verify that the model will fit or adequatelydescribe varying data sets from the building. According to one exemplaryembodiment, cross-validation module 218 may use a K-foldcross-validation method. The K-fold cross validation method isconfigured to randomly partition the historical data provided tobaseline calculation module 210 into K number of subsamples for testingagainst the baseline model. In other embodiments, a repeated randomsub-sampling process (RRSS), a leave-one-out (LOO) process, acombination thereof, or another suitable cross-validation routine may beused by cross-validation module 218.

Referring now to FIG. 3A, a flow chart of a process 290 for determiningobserved or measured variables to use in generation of a baseline modelis shown, according to an exemplary embodiment. Process 290 isconfigured to select observed variables based on predictor variablesgenerated by the data synchronization module of the baseline calculationmodule. Process 290 includes receiving data (step 291). Process 290further includes determining the largest period of time for which thereis data for predictor variables and observed variables (step 292). Theperiod of time determined in step 292 may represent a period of time forwhich there will be enough predictor variable data for the correspondingdata received in step 291. Step 292 may include, for example, removinginsufficient data points and determining the longest period for whichthere is enough data. For example, if there is too little data for oneday, it may be determined that a predictor variable for that day may notbe generated and therefore the day may not be used in ultimatelydetermining a baseline model.

Process 290 includes initializing the observed variable (step 293).Initializing the observed variable includes determining a start and endpoint for the observed variable data, determining the type of data andthe units of the data, and any other initialization step. Step 293 isused to format the received data from step 291 such that the observeddata is in the same format as the predictor variable data.

Process 290 includes determining if enough predictor variable dataexists (step 294). For example, if there is enough predictor variables(e.g., energy days) for a set period of time (e.g., 20 days), thenprocess 290 determines that the predictor variables and its associatedobserved variable (e.g., enthalpy) may be used for baseline modelgeneration.

Referring now to FIG. 3B, a flow chart of a process 300 for determiningcalculated variables to use in generation of a baseline model is shown,according to an exemplary embodiment. Selecting some calculatedvariables for inclusion in a regression analysis used to generate abaseline model may provide better results than selecting some othercalculated variables for inclusion, depending on the particulars of thebuilding and its environment. In other words, proper selection ofcalculated variables can improve a resultant baseline model's ability toestimate or predict a building's energy use. Improvements to energy useprediction or estimation capabilities can improve the performance ofalgorithms that rely on the baseline model. For example, an improvedbaseline model can improve the performance of demand responsealgorithms, algorithms for detecting abnormal energy usage, andalgorithms for verifying the savings of an energy conservation measure(e.g., M&V calculations, etc.).

Process 300 provides a general process for selecting calculatedvariables to use in generation of a baseline model. FIGS. 4A-4E providea more detailed view of process 300. The output of process 300 (and ofthe processes shown in FIGS. 4A-4E) is the selection of calculatedvariables to use to generate the baseline model. Particularly, in anexemplary embodiment, process 300 selects between cooling energy days,heating energy days, cooling degree days, and heating degree days. Theselection relies on a calculation of balance points (e.g., optimal basetemperatures or enthalpies) of a building and using the calculations tocalculate the potential variables (e.g., the energy days and degreedays) for selection into the set of variables used to generate thebaseline model.

The calculation and selection of calculated variables (for inclusioninto the baseline model generation) is based in part on calculatedbalance points and may be accomplished in different ways according todifferent exemplary embodiments. According to one embodiment, anonlinear least squares method (e.g., a Levenburg-Marquardt method) maybe used to find the best calculated variables. Such a method, forexample, may use daily temperature and energy meter readings tocalculate balance points. A nonlinear least squares method may then beapplied to the balance points to generate and select the appropriatecalculated variables.

According to another embodiment, an optimization scheme may be used todetermine the best balance point or points. The optimization scheme mayinclude an exhaustive search of the balance points, a gradient descentalgorithm applied to the balance points to find a local minimum of thebalance points, a generalized reduced gradient method to determine thebest balance point, and a cost function that is representative of thegoodness of fit of the best balance point. The cost function may be anestimated variance of the model errors obtained from an iterativelyreweighted least squares regression method, according to an exemplaryembodiment. The iteratively reweighted least squares regression methodis configured to be more robust to the possibility of outliers in theset of balance points generated and can therefore provide more accurateselections of calculated variables.

The optimization scheme algorithm may also use statistics (e.g., at-statistic representative of how extreme the estimated variance is) todetermine if building energy use is a function of, for example, heatingor cooling. The statistics may be used to determine which balance pointsto calculate as necessary (e.g., calculating balance points relating toheating if statistics determine that building energy use is based onheating the building.

Referring to FIG. 3B and FIGS. 4A-4E, an optimization scheme isdescribed which uses a golden section search rule to calculate energydays and degree days and to determine which of the calculated variablesto use based on a statistics to determine the type of energy use in thebuilding.

Process 300 includes receiving data such as temperature data, humiditydata, utility meter data, etc. (step 302). Process 300 further includesusing the received data to calculate possible balance points (step 304).For example, step 304 may include using the received temperature dataand humidity data to calculate an enthalpy. As another example, step 304may include determining an optimal base temperature using the receivedtemperature data. Process 300 further includes steps 306-318 fordetermining a calculated variable to use for baseline model generationbased on enthalpy and temperature data calculated in step 304; accordingto various exemplary embodiments, steps 306-318 of process 300 may beused to determine calculated variables based on other types of balancepoints.

Process 300 includes steps 306-310 for determining optimal predictorvariables based on the enthalpy calculated in step 304. Process 300includes determining a type of baseline model for cooling or heatingenergy days using the enthalpy (step 306). Process 300 further includesfinding an optimal enthalpy balance point or points and minimum errorvariance for the resultant cooling and/or heating energy days (step308). The optimal enthalpy balance point relates to, for example, apreferred base enthalpy of the building, and the minimum error variancerelates to the variance of the model errors at the optimal balance point(determined using IRLS). Process 300 further includes determining if theoptimal predictors determined in step 308 are significant (step 310).

Process 300 includes steps 312-316 for determining optimal predictorvariables based on a temperature (e.g., temperature data received instep 302 by baseline calculation module 210). Process 300 includesdetermining a type of baseline model for cooling or heating degree daysusing the temperature (step 312). Process 300 further includes findingan optimal temperature balance point and minimum error variance for thecooling and/or heating degree days (step 314). Process 300 also includesdetermining if the optimal predictors determined in step 314 aresignificant (step 316). Using the results of steps 306-316, process 300determines which of energy days and degree days yields a better (e.g.,more accurate) baseline model (step 318) when used by baselinecalculation module 210.

Referring now to FIGS. 4A-4E, a detailed flow chart of process 300 ofFIG. 3B is shown, according to an exemplary embodiment. Process 400 ofFIGS. 4A-4E is shown using enthalpy and temperature to determine thebalance points. The balance points are used to calculate the optimaldegree or energy days predictor variable and in determining whichcalculated variables to use for baseline model generation. According toother embodiments, other methods may be used to determine the balancepoints. Referring more specifically to process 400 shown in FIG. 4A,process 400 may calculate an enthalpy using temperature data input 402and humidity data 404 (step 408). According to an exemplary embodiment,enthalpy may be calculated using a psychometric calculation. Process 400includes receiving meter data 406 and enthalpy data and averages thedata over all periods (step 410) for use in the rest of process 400.

Process 400 includes determining possible baseline model types (i.e.,whether both the heating and cooling balance points are needed todescribe energy use in the building) based on the calculated enthalpy(step 412). For example, step 412 includes the method of determining apredictor variable associated with minimum energy use and then sortingall of the calculated variables (e.g., the variables determined in steps408-410) and finding where the minimum energy predictor variable rankscompared to the other predictor variables.

Process 400 includes determining if using cooling base enthalpy in thebaseline model calculation is feasible (step 414). If the predictorvariable associated with the minimum energy found in step 412 is closeenough to the maximum calculated variable, then it may be determinedthat a cooling base does not exist because cooling does notsignificantly impact energy consumption in the building or it cannot befound due to lack of data. If using the cooling base enthalpy is notfeasible, the cooling base enthalpy is set to NaN and the minimum sigmais set to infinity (step 428) such that both values will be “ignored”later by process 400.

If using a cooling base enthalpy is feasible, a range of feasiblecooling base enthalpies is set (step 416). The range may vary from themaximum average monthly enthalpy to ten units less than the predictorvariable associated with the minimum energy use.

Process 400 includes finding the base temperature of the predictorvariable (e.g., via balance point module 264) by finding the baseenthalpy for which the estimated variance of the regression errors isminimized. According to one exemplary embodiment, the minimization maybe performed using the golden section search rule. Process 400 includesinitializing the golden section search (step 418) and iterating thegolden section search (step 420) until a desired base tolerance has beenreached (step 422). The base tolerance may be predetermined via alogarithmic function of the size of the range, according to an exemplaryembodiment. The golden section search of steps 420-422 provides anoptimal balance point. The optimal balance point is then used tocalculate a measure of variability and determine the t-statistic for thepredictor variable.

When a desired base tolerance has been reached for the golden sectionsearch (step 422), process 400 may determine whether the t-statistic issignificant (step 424). If the t-statistic is not significant, theminimum sigma representative of the t-statistic is set to infinity (step428). If the t-statistic is significant, it is used in a later step ofprocess 400 to determine the best predictor variable to use for thebaseline model.

Referring now to FIG. 5, the objective function used in the goldensection search of FIGS. 4A-4E is shown in greater detail, according toan exemplary embodiment. Process 500 is configured to calculate theobjective function for use in the golden section search. Process 500includes receiving data from, for example, step 416 of process 400relating to a range of enthalpies or temperatures (or othermeasurements) that may be used for the baseline model. Process 500includes, for all periods, finding an average predictor variable foreach given balance point (step 502). For example, the following integralmay be used to find the predictor variable:

$\frac{1}{T}{\int_{periodstart}^{periodend}{{\max( {0,{{X(t)} - b}} )}{dt}}}$while the following integral may be used to determine the averageresponse variable:

$\frac{1}{T}{\int_{periodstart}^{periodend}{{Y(t)}{dt}}}$where b is the balance point and T is the length of the period.

After obtaining the predictor variable, process 500 includes performingan iteratively reweighted least squares method (IRLS) (step 504). IRLSis used because it is more robust to outliers than standard OLS methods.Process 500 includes using the results of step 504 to obtain an estimateof the error variance (step 506) which is used by process 400 todetermine the predictor variable with the best fit for generating abaseline model.

Referring back to FIGS. 4A-4B, process 400 further includes repeatingthe steps of steps 414-428 for the heating base enthalpy instead of thecooling base enthalpy. Referring now to FIG. 4B, process 400 includesdetermining if heating base enthalpy is feasible (step 430), setting arange of feasible heating base enthalpies (step 432), initializing agolden section search (step 434), iterating the golden section search(step 436) until a desired base tolerance is reached (step 438), anddetermining if the t-statistic is significant (step 440). If thet-statistic is not significant, the minimum sigma is set to infinity(step 444), and if otherwise, the t-statistic will be used later inprocess 400.

Process 400 further includes repeating the steps shown in FIGS. 4A-B,only for the temperature instead of the enthalpy. Referring now to FIG.4C, process 400 includes determining possible model types based on thetemperature data (step 446). Process 400 further includes determining ifcooling base temperature is feasible (step 448), setting a range offeasible cooling base temperatures (step 450), initializing a goldensection search (step 452), iterating the golden section search (step454) until a desired base tolerance is reached (step 456), anddetermining if the t-statistic is significant (step 458). Referring nowto FIG. 4D, process 400 includes determining if heating base temperatureis feasible (step 464), setting a range of feasible heating basetemperatures (step 466), initializing a golden section search (step468), iterating the golden section search (step 470) until a desiredbase tolerance is reached (step 472), and determining if the t-statisticis significant (step 474). If the t-statistic is insignificant foreither, the cooling or heating base temperature is set to NaN and theminimum sigma for the cooling or heating base temperature is set toinfinity (steps 462, 478 respectively).

Process 400 is then configured to recommend a predictor variable basedon the base temperatures and minimum sigmas determined in the process.Process 400 includes recommending a default cooling degree daycalculation (step 484) as a predictor variable if both the cooling basetemperature and cooling base enthalpy were both set to NaN in process400 (step 480). Process 400 may also recommend cooling degree days as apredictor variable if the minimum sigma for cooling energy days isbetter (e.g., lower) than the minimum sigma for cooling degree days(step 482). Otherwise, process 400 recommends using cooling energy days(step 486).

Process 400 may repeat steps 488-494 for heating degree days and heatingenergy days. Process 400 includes recommending a default heating degreeday calculation (step 492) as a predictor variable if both the heatingbase temperature and heating base enthalpy were both set to NaN inprocess 400 (step 488). Process 400 may also recommend heating degreedays as a predictor variable if the minimum sigma for heating energydays is better than the minimum sigma for heating degree days (step490). Otherwise, process 400 recommends using heating energy days (step494).

Referring now to FIG. 6, a flow chart of a process 600 of calculatingenthalpy is shown, according to an exemplary embodiment. Process 600includes receiving temperature and humidity data (step 602). Step 602may further include identifying and removing humidity data points thatare NaN, converting temperature data points to the correct format, orany other pre-processing steps.

Process 600 further includes, for each temperature data point, finding acorresponding humidity data point (step 604). For example, for a giventime stamp for a temperature data point, step 604 includes searching fora corresponding time stamp for a humidity data point. According to anexemplary embodiment, a humidity data point with a time stamp within 30minutes (or another period of time) of the time stamp of the temperaturedata point may be chosen as a corresponding humidity data point. Step604 may further include searching for the closest humidity data pointtime stamp corresponding with a temperature data point time stamp. If acorresponding humidity data point is not found for a temperature datapoint, an enthalpy for the time stamp of the temperature data point isnot calculated.

Process 600 further includes, for each corresponding temperature andhumidity data point, calculating the enthalpy for the corresponding timestamp (step 606). The enthalpy calculation may be made via a nonlineartransformation, according to an exemplary embodiment. The calculationincludes: converting the temperature data into a Rankine measurement andcalculating the partial pressure of saturation using the below equation:

${pws} = {\exp\{ {\frac{C_{1}}{T} + C_{2} + {C_{3}T} + {C_{4}T^{2}} + {C_{5}T^{3}} + {C_{6}T^{4}} + {C_{7}{\ln(T)}}} \}}$where C1 through C7 are coefficients and T is the temperature data. Thecoefficients may be, for example, based on ASHRAE fundamentals. Theenthalpy calculation further includes: calculating the partial pressureof water using the partial pressure of saturation:

${pw} = \frac{H}{100*{pws}}$where H is the relative humidity data. The enthalpy calculation furtherincludes calculating the humidity ratio:

$W = \frac{0.621945*{pw}}{p - {pw}}$where W is in terms of pounds water per pound of dry air. The enthalpycalculation further includes the final step of calculating the enthalpyin BTUs per pound dry air:Enthalpy=0.24*T+W*(1061+0.444*T)Once the enthalpy is calculated, the enthalpy is used rather thantemperature data or humidity data in regression analysis to generate thebaseline model (step 608).

Detecting Changes in Energy Use in the Building to Support Measurementand Verification Systems and Methods

As described above, the predictor variables for use for theabove-described baseline model may generally be calculated as describedabove by the equation:

x = ∫_(t₁)^(t₂)max (0, p(t) − b)dtwhere x is the synchronized predictor variable, p(t) is the inputpredictor variable, t₁ and t₂ represent the start and end points for theperiod of time, and b is the balance point. The synchronized predictorvariables are then used to develop the linear model:ŷ _(k)=β₀ t _(k)+β₁ x _(1,k)+β₂ x _(2,k)+ . . . +β_(p) x _(p,k)where t_(k) is the time duration of the period and β_(q) is the q^(th)marginal energy cost per synchronized predictor variable x_(q,k). One ormore of the systems or methods described above may be used to developthe linear model and to select appropriate predictor variables for thebaseline model.

Once the baseline model is developed, it is used, e.g., during areporting period, to estimate the amount of energy that would have beenused during the reporting period had the ECM not been performed. Theavoided energy use can be described and calculated using:energy avoidance=BE±RA±NRA−RPEwhere BE is the baseline energy (e.g., average energy use during thebaseline), RA are the routine adjustments (e.g., adjustments based onthe predictor variables and typically included in the baseline model),NRA are the non-routine adjustments (e.g., adjustments based on(typically constant) factors related to energy usage that are notincluded in the baseline model), and RPE is the reporting period energy(e.g., the actual reported energy usage in the building). If there areno non-routine adjustments, the energy avoidance can be represented as:energy avoidance=ŷ _(k)−RPEwhere ŷ_(k) is the estimated energy use using the baseline model, i.e.,the estimated energy use (BE) plus the routine adjustments based on thereporting period predictor variables (RA).

If the need for non-routine adjustments is identified (e.g., if staticfactors have changed in a way such that energy use is affected), thennon-routine adjustments must be performed by taking them into account inthe energy avoidance calculation. Changes in static factors that couldnecessitate non-routine adjustments may include, for example, changes inthe size of the used facility space, the hours of operation of thebuilding, the number of employees using the building, and the number ofcomputer servers in the building. Any factor not expected to normallychange and having an impact on energy use that is not included in thepredictor variables of the baseline model could be considered a staticfactor. A change in static factors may occur during the reporting periodor during the baseline period.

Referring generally to the figures below, systems and methods aredescribed for automatically detecting changes in static factors during abaseline period or reporting period. A method for automaticallydetecting changes in static factors may include developing a nullhypothesis of a baseline model with constant coefficients (stationary).The method further includes determining whether a statisticallysignificant change in the baseline model's parameters has occurred. Ifstatistically significant changes in the baseline model's parameters aredetected over time, the method can include outputting an indication(e.g., to a user interface, to a computerized module for recalculatingthe baseline model, etc.) that a static factor has changed. The methodcan include conducting multiple sequential hypothesis tests spread overa period of time. Known theoretical correlations between temporallyadjacent test statistics can be used to provide false positivesuppression (e.g., suppress an indication that a static factor haschanged where the data causing such an indication was spurious due toweather or another transient condition) without the undesirable effectof greatly reducing the test power (as would occur if, for example, theBonferroni correction was applied).

When a building is operating in a consistent manner (e.g., consistentenergy usage) and the baseline model for the building includes all theindependent predictor variables necessary to accurately estimate theenergy usage, the coefficients of the baseline model should remainconstant over time. Therefore, if two temporally consecutive windows ofdata from time intervals [t_(a), t_(b)] and [t_(b), t_(c)] are used, thedifference in two baseline model coefficients should be near zero. Thedifference in model coefficients can be represented as:Δβ={circumflex over (β)}₁−{circumflex over (β)}₂where Δβ is the difference between the baseline model coefficients fromwindow one and window two {circumflex over (β)}₁, {circumflex over(β)}₂, respectively. Because the baseline model coefficients havephysical meaning (e.g., cost per cooling degree day), unexpected changesin coefficients over time can advantageously be linked to root causes(e.g., chiller fouling, decrease in setpoints, etc.).

For the coefficients, there may be random variation in a coefficient themagnitude of which is based on, for example: the number of periods ordata points in the time intervals, the variance of the errors of thebaseline model, the number of predictor variables used in the model, andthe values of the predictor variables during each of the two timeintervals. Additionally, the values of the predictor variables duringeach time interval can have a significant effect of the variation of thecoefficients. Thus, in the preferred embodiment, the statistical methodsdescribed below may be used to determine whether the difference in thecoefficients is large enough to be considered statistically significantor whether the coefficient difference is due to the random variationdescribed above, rather than a real change in a static factor affectingthe building's energy use.

Referring now to FIG. 7, a flow chart of a process 700 for automaticallydetecting changes in static factors that occurred between two sequentialtime periods is shown, according to an exemplary embodiment. Process 700includes developing a null hypothesis that the baseline model isaccurate (e.g., the baseline model is a correct representation ofbuilding energy usage) and stationary (e.g., the coefficients areconstant) (step 702).

Process 700 further includes calculating the baseline model includingthe baseline model coefficients for two different chronologicallyordered data sets (step 704). The coefficients may include, for example,energy use per degree day or energy use per energy day. In otherembodiments, the coefficients could include an aggregation, or othervalue that describes the coefficients for a given periodic baselinemodel. In step 705, process 700 includes calculating at least one teststatistic that is related to the difference in a coefficient or a set ofcoefficients from the two data sets. If two consecutive windows of dataare used to build similar baseline models (i.e., the coefficients of themodels are similar) and static factor changes have not occurred duringthe time period of the windows, then the test statistic should be small(i.e., within the expected amount of random variation).

Process 700 further includes determining a probability density function(PDF) of the test statistic (step 706). Using the PDF of the teststatistic, process 700 determines a critical value such that theprobability of the test statistic being greater than the critical valueis the same as the specified probability of falsely rejecting the nullhypothesis of step 702 (step 708). A user of the building managementsystem may select an acceptable level for the probability of falselyrejecting the null hypothesis, according to an exemplary embodiment. Insome embodiments, the probability may be built into contract standardsand differ from building to building, building type to building type, orotherwise vary.

Process 700 further includes rejecting or accepting the null hypothesisbased on whether the test statistic is greater than the critical valuefound in step 708 (step 710). If the test statistic is greater than thecritical value, then the null hypothesis of a constant baseline model isrejected. When the null hypothesis is rejected, the system can determinethat a static factor has changed and a non-routine adjustment may benecessary. The user may be alerted to the necessity of a non-routineadjustment, additional calculations may be performed, or other variousactions related to the non-routine adjustment may be performed by thebuilding management system.

Referring further to process 700, least squares estimation may be usedto find the coefficients of the baseline models. The least squaresestimation problem can be stated as the following: given a linear modelY=Xβ+ε, ε˜N(0,σ² I)find the vector {circumflex over (β)} that minimizes the sum of squarederror RSS:RSS=∥Y−X{circumflex over (β)}∥ ².

In the above equations, Y is a vector that contains the individual nobservations of the dependent variable and X is a n by p+1 matrix thatcontains a column of ones and the p predictor variables at which theobservation of the dependent variable was made. ε is a normallydistributed random vector with zero mean and uncorrelated elements.According to various exemplary embodiments, other methods than using RSSmay be used (e.g., weighted linear regression, regression through theorigin, etc.) The optimal value of {circumflex over (β)} based on theleast squares estimation has the solution stated above with respect toFIG. 2:{circumflex over (β)}=(X ^(T) X)⁻¹ X ^(T) Yand {circumflex over (β)} is a normal random vector distributed as:{circumflex over (β)}˜N(β,σ²(X ^(T) X)⁻¹).The resulting sum of squared error divided by sigma squared is achi-square distribution:

$\frac{RSS}{\sigma^{2}} \sim {\chi_{n - {({p + 1})}}^{2}.}$

The difference in the coefficients is distributed asΔβ={circumflex over (β)}₁−{circumflex over (β)}₂ ˜N(0,σ²[(X ₁ ^(T) X₁)⁻¹+(X ₂ ^(T) X ₂)⁻¹]).The quadratic form of a normally distributed random vector where thesymmetric matrix defining the quadratic form is given by the inverse ofthe covariance matrix of the normal random vector is itself a chi-squaredistributed random variable with degrees of freedom equal to the lengthof Δβ:

$\frac{\Delta\;{\beta^{T}\lbrack {( {X_{1}^{T}X_{1}} )^{- 1} + ( {X_{2}^{T}X_{2}} )^{- 1}} \rbrack}^{- 1}\Delta\;\beta}{\sigma^{2}} \sim {\chi_{p + 1}^{2}.}$Additionally, the sum of two independent chi-square distributions isitself a chi-square distribution with degrees of freedom equal to thesum of the degrees of freedom of the two original chi-squaredistributions. Thus, the sum of the two sum of squared errors divided bythe original variance is chi-square distributed, as:

$\frac{{RSS}_{1} + {RSS}_{2}}{\sigma^{2}} \sim {\chi_{n_{1} + n_{2} - {2{({p + 1})}}}^{2}.}$n₁ and n₂ are the number of data points used to estimate the modelcoefficients {circumflex over (β)}₁, {circumflex over (β)}₂. Finally,the ratio of two chi-square distributions divided by their respectivedegrees of freedom is an F-distributed random variable:

$F_{\Delta\;\beta} = {{( \frac{\Delta\;{\beta^{T}\lbrack {( {X_{1}^{T}X_{1}} ) + ( {X_{2}^{T}X_{2}} )^{- 1}} \rbrack}^{- 1}\Delta\;\beta}{{RSS}_{1} + {RSS}_{2}} )( \frac{n_{1} + n_{2} - {2( {p + 1} )}}{p + 1} )} \sim {F_{{p + 1},{n_{1} + n_{2} - {2{({p + 1})}}}}.}}$F_(Δβ) is defined as the test statistic. As Δβ moves away from theorigin, F_(Δβ) increases. Further, the maximum increase occurs in thedirection of the least variance of the model coefficients and is scaledby the sum of squared errors. Thus, F_(Δβ) is based on changes in modelcoefficients which can easily be related back to a root cause and ittakes into account the random variation of the changes of the modelcoefficients even when the model is stationary. The F_(Δβ) statistic mayfurther be converted into a standard normal variable Z_(Δβ) by theproper transformation function.

The F_(Δβ) or Z_(Δβ) statistic can be used as the test statistic in step710 of process 700 (if Z_(Δβ) is used then the critical value will becalculated with the inverse distribution function of the standard normaldistribution). The user of the building (or a contract or automatedprocess) determines an acceptable level for α, the probability ofrejecting the null hypothesis when it is in fact valid. An automatedprocess uses α to determine the critical value for use in accepting orrejecting the null hypothesis. The null hypothesis is rejected if theF-statistic F_(Δβ) is greater than its critical value f_(crit) which maybe calculated using F⁻¹ _(p+1, n) ₁ _(+n) ₂ _(−2(p+1))(1−α), where F⁻¹is the inverse of the cumulative F-distribution with the requireddegrees of freedom. In other words, the null hypothesis is rejected anda static factor can be determined to have changed when F_(Δβ)>f_(crit).

Process 700 is used to determine whether a single test statistic, from asingle test, including the model coefficients of two (time adjacent)building models, indicates a change in static factors in a building.Process 700 is more particularly used to determine whether a change hasoccurred at any point in time during the baseline period or reportingperiod. In an exemplary embodiment, process 700 is repeated multipletimes over sets of data that are windowed using different methods basedon whether the baseline period data (model building) or reporting perioddata (facility monitoring) is being tested for static factor changes(i.e., model coefficient consistency). Two representative differentmethods are shown in greater detail in FIGS. 8A and 9A. In these cases,the critical value must be adjusted from that shown above to limit thefalse alarm rate of any of the multiple tests performed.

When attempting to detect static factor changes during the baseline orreporting period, several iterations of model calculations andassociated coefficient testing can be conducted. For example, over thebaseline data, the test may be run n−2(p+1)+1 times, where n is thenumber of data points and p is the number of predictor variables in eachmodel. If that many tests are run, the probability of finding at leastone false alarm will be much greater than α (in fact, as the number oftests increase, the probability approaches 1). Therefore, for thebaseline period, some embodiments limit the probability that any of theseveral tests falsely reject the null hypothesis to avoid false alarms(i.e., false indications that a static factor of a baseline model haschanged), since several tests will be performed. For the reportingperiod, limiting the probability of a false alarm in a set time durationto a predefined value can help avoid falsely rejecting the nullhypothesis. For example, the user or electronics (e.g., based on minimumperformance or contract specification) may specify that the probabilityof a false rejecting the null hypothesis on any test performed in agiven year should be less than 15%.

Referring now to FIG. 8A, a flow chart of a process 800 for detectingchanges in static factors of a baseline model during a baseline periodis shown, according to an exemplary embodiment. To detect changes, theZ_(Δβ) statistic is calculated at each possible value for the dividingtime between the two data windows, and all baseline data (e.g., all datafrom the beginning of the baseline period to the beginning of theinstallation period) is used. That is, the statistic is calculated foreach possible windowing (for which the data remains in chronologicalorder and there is at least one degree of freedom in each window) in thebaseline period.

Process 800 includes temporally windowing the data into two windows(step 802). During the baseline period, the data received and used inthe baseline model is received by process 800 and divided into twodifferent time periods (i.e., windows). The data is placed into the datawindows in chronological order (e.g., into the first window until thefirst window fills, then the second window). Referring also to process850 of FIG. 8B, a process for determining the size and time periods forthe data windows may be used. Baseline model coefficients may becalculated for the two different data windows, and the difference incoefficients Δβ is calculated (step 804). Step 804 may further includestoring the difference (e.g., for consideration in step 820 of process800). Process 800 further includes calculating the test statistic F_(Δβ)based on the change in model coefficients (step 806). Process 800further includes converting the test statistic F_(Δβ) into a standardnormal random variable Z_(Δβ) (step 808). The conversion is done bypassing the test statistic through its cumulative probabilitydistribution and then through the inverse of the standard normaldistribution. The resultant standard normal random variable Z_(Δβ) isgiven by:Z _(Δβ) =N _(0,1) ⁻¹ {F _(p+1, n) ₁ _(+n) ₂ _(−2(p+1))(F _(Δβ,k))}.

Process 800 further includes determining whether to slide the datawindow (step 810). After calculating a difference in step 804 for twogiven time periods, the data windows for the two given time periods maybe adjusted. For example, for two time periods defined by [t_(a), t_(b)]and [t_(b), t_(c)], step 810 of sliding the data window is executed byincreasing t_(b), increasing the first data window by one data point anddecreasing the second data window by one data point. Sliding the datawindow may include moving data points from one time period to anotherbased on a timestamp or other property of each individual data point. Bysliding the data window (e.g., by moving the end point of the first datawindow and the start point of the second data window), new coefficientsfor the data windows may be calculated, and another difference incoefficients and Z-statistic may be calculated. Process 800 repeatssteps 802-808 until, for example, the second data window contains thefewest allowable data points (e.g., the second window has p+2 datapoints). For example, process 800 may repeat steps 802-808, beginningfrom when the first data window contains the fewest number of possibledata point until the second data window contains the fewest number ofpossible data points. The method of repeating steps 802-808 by shiftingthe data windows is shown in greater detail in FIG. 8B.

Referring now to FIG. 8B, the shifting of the data windows forcalculating a new observed Z_(Δβ) statistic is shown in greater detail.The detailed process of FIG. 8B may be the steps that occur during steps802-806 of FIG. 8A. Process 850 includes receiving response variables Y(e.g., variables representative of building energy usage) 852 andbuilding predictor variables X 854. Process 850 includes setting aninitial first window end of p+2, where p is the number of predictorvariables (step 856). Such a choice means that the initial first datawindow only includes p+2 data points (enough for one degree of freedomin the probability distribution calculations). The first window endmarks the last data point that is in the first window; thus, the nextdata point is the first datum in the second window. If the observedmaximum Z_(Δβ) statistic indicates that a static factor has changed,this point can be inferred to represent a point at or after which astatic factor change occurred. For example, when coefficients arecalculated using the two data windows and a difference in coefficientsis large enough, the data point that is used as the endpoint or startpoint for the data windows may be determined to represent a time at orafter which a potential change in static factors occurred. Inalternative embodiments, the different windows could include gapsbetween the windows (e.g., data points “in between” the data points inthe two windows) or some amount of overlap.

Process 850 further includes, while the first window end is less than orequal to n−p+2, increasing the first window end by one (e.g., slidingthe first data window endpoint by one) (step 858). n is the number ofdata points in the entire baseline period being used. The data isdivided into the two data windows based on the endpoints (step 860).Coefficients are calculated for each of the data windows (steps 862,864). In step 862, coefficients for the response variables Y for the twodata windows are calculated, and in step 864, coefficients for thepredictor variables X for the two data windows are calculated. Thedifference in coefficients and the Δβ statistic is calculated (step866), the F-statistic F_(Δβ) is calculated (step 868), and thestandardized statistic Z_(Δβ) is calculated (step 870). Steps 866-870corresponds with steps 804-808 of process 800. Process 850 is thenrepeated until the second data window is eventually reduced to only p+2data points.

Referring again to FIG. 8A, after calculating the statistics, process800 includes determining the maximum Z-statistic from steps 802-808(step 812). Step 812 includes comparing the differences calculated instep 808 (e.g., or step 870) to find the maximum difference. Process 800further includes calculating the P-value of the maximum Z-statistic(step 814). The P-value represents the probability of obtaining amaximum test statistic as extreme as the observed maximum Z-statisticdetermined in step 812 given that the null hypothesis (linear model withconstant coefficients) is true.

The P-value may be calculated in various ways. In one exemplaryembodiment, the P-value may be approximated using a Monte Carlosimulation. In one embodiment, Monte Carlo samples of the maximum Z_(Δβ)determined in step 812. For example, steps 808-812 are performed oncomputer generated data that fits the null hypothesis, samples whichhave a maximum value greater than the data points used in process 800may be determined, and the fraction or ratio of such samples may be usedto calculate the P-value. In another exemplary embodiment, the P-valuemay be approximated using a Monte Carlo simulation of a multivariatenormal vector. One exemplary use of a Monte Carlo simulation tocalculate a P-value (e.g., step 814) is shown in greater detail in FIG.13.

Process 800 further includes displaying the P-value to a user anddisplaying alarms or warnings based on the P-value if necessary (step816). The P-value is representative of the likelihood of a change in thestatic factors of the building (e.g., a P-value near zero indicated thata change may have occurred). Step 816 includes using the P-value togenerate an alarm or warning representative of the likelihood of thechange. Step 816 may also or alternatively include providing the P-valueto another system component or automated process (e.g., recalculatingthe baseline model with knowledge of the changed static factorlikelihood). The energy cost or savings is determined if a changeoccurred (step 818). An adjustment to account for the change in staticfactors is also determined (step 820). Steps 818-820 may includeestimations of energy cost or savings (e.g., based on the changed staticfactors and/or updated model) and adjustments based on the P-value.

Referring now to FIG. 9A, a flow chart of a process 900 for detectingchanges in static factors of a baseline model during a reporting period(as opposed to during a baseline period as described above withreference to FIGS. 8A-B) is shown, according to an exemplary embodiment.The reporting period may last as long as twenty years or more andseveral changes in static factors may occur over the time frame. Process900 may be used once enough data is collected by the building managementsystem, and after enough data is collected, old data may be discardedfrom process 900 as new data continues to be collected.

Process 900 includes calculating a critical value for the test statisticat a given alpha value (step 902). The alpha value can be supplied bythe user or obtained from the standards of the contract and may varyfrom building to building or building type to building type. Accordingto one embodiment, step 902 includes using a Monte Carlo cumulativeprobability distribution inversion to calculate the critical value. Anexemplary method and use of such an inversion to find the critical valueis shown in greater detail in FIG. 12. The samples used in the inversionmay be performed using the actual data points (formed by simulating alinear model and calculating Z_(Δβ)) or may be simulated from amultivariable normal distribution.

Process 900 further includes aligning the two data windows with the lastchange (step 904). Step 904 includes receiving new data and sliding thedata in the two windows. Data for the building during the reportingperiod is collected until two data windows are filled (step 906). In anexemplary embodiment, a window may not be considered full until aminimum amount of time has elapsed (e.g., one month, one year, etc.).The data in each window is then shifted forward (step 908) (e.g., asdescribed in process 950 of FIG. 9B) and a normalized test statistic forthe two data windows is calculated (step 910). Step 910 may include, forexample, calculating coefficients for the two data windows anddetermining the difference in the coefficients as described in process700 of FIG. 7.

Process 900 further includes determining whether the test statisticcalculated in step 910 is greater than the critical value determined instep 902 (step 912). If the test statistic is greater than the criticalvalue, the user may be alerted to a change in static factors or anindication of the change in static factors may be communicated (step914) to another module of the building management system (e.g., areporting module, a logging module, a module for automaticallyinitiating recalculation of the energy model, etc.) and an energy costor savings is determined (step 916). An adjustment to account for thechange is determined as well (step 918). After the change or adjustmenthas completed, building response and predictor variables may becollected again until the initial windows are filled again. In otherwords, process 900 may repeat accordingly to continuously evaluatestatic factor changes in the utilized baseline model.

If the difference in step 910 is less than the critical value, process900 repeats steps 908-910 by shifting the two data windows. The methodof shifting the two data windows is shown in greater detail in FIG. 9B.

Referring now to FIG. 9B, the shifting of the data windows forcalculating an new observed standardized statistic of step 908 ofprocess 900 is shown in greater detail. Process 950 is configured tokeep both data windows used at a fixed length, and moves the datawindows forward in time instead of changing the size of either window.Therefore, when a new reporting period data point is obtained and used,the data point will become the last data point in the second datawindow, and the oldest data point in the first data window winds upbeing eliminated from the analysis.

Process 950 includes receiving a first window size, a second window sizen₂, and endpoints n₁, n₂ (step 952). Process 950 includes using theendpoints for the two data windows (step 954). The first window size andsecond window size are the sizes of the data windows that are preferredfor process 900. The two sizes of the data windows will remain fixedthroughout process 900, and process 950 includes changing the endpointsof the data windows instead of the size of the data windows. The sizesof the data windows may be predetermined by the building managementsystem or a user thereof. The endpoints n₁ and n₂ are used to determinethe initial endpoints of the two windows (e.g., the first windowincludes all data points from a cutoff point to point n₁, and the secondwindow includes all data points from n₁+1 to n₂.

While the second window end is less than the reporting end (e.g., theend data point for the reporting period), the first window end n₁ andsecond window end n₂ are increased by one (step 956). The data receivedby process 950 is then divided into two windows with start points andendpoints defined by the first window end and second window end (step958). Since the size of the two data windows are limited (e.g., based onthe first window size and second window size specified in step 952), the“oldest” data point that used to be in the first data window in theprevious iteration of process 950 no longer fits in the data window.Therefore, the data points in the first data window may range from avalue z to the new n₁. The second data window may continually beadjusted such that it includes data points from the new n₁+1 to the newn₂.

Coefficients are calculated for each of the data windows (steps 960,962). In step 960, the response variables are placed into two vectorscorresponding to the two windows, and in step 962, the predictorvariables are placed into two matrices corresponding to the two windows.The difference in coefficients (and the Z_(Δβ) statistic) is calculated(steps 964-968). Process 950 is then repeated as new data is gatheredthroughout the reporting period until the second data window reaches thereporting period end. The Z_(Δβ) statistic is then used in thecomparison of step 912 of process 900.

Referring now to FIG. 10, a block diagram of baseline calculation module210 is shown in greater detail, according to an exemplary embodiment.Baseline calculation module 210 is shown to include the data clean-upmodule, data synchronization module, regression analysis module, andcross-validation module as described in FIG. 2. Baseline calculationmodule 210 further includes data window module 1002, coefficientcalculation module 1004, and test statistic module 1006. Baselinecalculation module 210 is configured to, using modules 1002-1006, detectchanges in static factors during a baseline period. Baseline calculationmodule 210 further includes predictor variables 1010 which may beprovided to the various modules of the computer system (e.g., facilitymonitoring module 230).

Data window module 1002 is configured to perform the data window slidingand calculations as described in FIGS. 8B and 9B. Data window module1002 may receive data and either receive or determine initial datawindows for the process of FIG. 8B to use. Data window module 1002further slides the data windows (e.g., via processes 850, 950) andorganizes the data in the two windows to facilitate the completion ofthe processes of FIG. 8B.

Coefficient calculation module 1004 is configured to calculate thecoefficients for the baseline model and for the detection of changes instatic factors. Coefficient calculation module 1004 may be configuredto, for example, conduct the calculations shown in steps 862 and 864 ofFIG. 8B and steps 960 and 962 of FIG. 9B.

Test statistic module 1006 is configured to receive coefficientcalculation results from module 1004 and to calculate a test statisticZ_(Δβ) representative of the difference in coefficients. The calculationmay include the calculation of the F-statistic F_(Δβ) which is thenconverted into the test statistic Z_(Δβ). Test statistic module 1006 mayfurther be configured to calculate a PDF of the test statistic Z_(Δβ)and a PDF of the maximum of n test statistics Z_(Δβ).

Baseline calculation module 210 further includes energy savings module1008. Energy savings module 1008 is configured to determine the energy(or monetary) costs or savings associated with a change in staticfactors.

One method of estimating the cost or savings is to take data from thelast detected change (or the end of the installation period) to the timethe change was detected and the data available since the changeoccurred. Model coefficients can be calculated for both the pre- andpost-change data. The estimated cost is then given by:cost=x ^(T)[β_(new)−β_(old)]with a standard error of:se(cost)={circumflex over (σ)}{1+x ^(T)[(X _(old) ^(T) X _(old))⁻¹+(X_(new) ^(T) X _(new))⁻¹]x} ^(1/2).where β_(new), β_(old) are the calculated coefficients, X_(old) is datafrom before the change, and X_(new) is data from after the change.

Referring now to FIG. 11, a detailed block diagram of the facilitymonitoring module 230 of FIG. 1C is shown, according to an exemplaryembodiment. Facility monitoring module 230 is configured to detectchanges in static factors during the reporting period. Module 230receives baseline model information (e.g., predictor variables 1010)from baseline calculation module 210 and other building information suchas false alarm probability data 1120 and other building data. Falsealarm probability data 1120 may be a user entered value that representsa desired probability of generating a false alarm of determining that astatic factor has changed when it actually has not. For example, 5% maybe a desired probability. False alarm probability data 1120 may furtherbe received from system feedback module 234, baseline agreementinformation 232, or otherwise.

Facility monitoring module 230 includes critical value module 1102.Critical value module 1102 is configured to calculate a critical valuefor the Z_(Δβ) statistic. Critical value module 1102 may be configuredto perform a cumulative probability distribution inversion calculationas described in FIG. 12, according to an exemplary embodiment.

Facility monitoring module 230 further includes data window module 1104,data collection module 1106, and test statistic module 1108. Data windowmodule 1104 is configured to determine initial data window parametersfor the processes of FIGS. 8B and 9B and may have the same functionalityas data window module 1002 of FIG. 10. Data collection module 1106collects building data and provides the data to data window module 1104for filling the two data windows. Test statistic module 1108 isconfigured to calculate a test statistic using predictor variables 1010.Test statistic module 1108 may calculate the test statistic may have thesame functionality as test statistic module 1006 of FIG. 10.

Facility monitoring module 230 further includes energy savings module1110 and reporting module 1112. After calculating the test statistic anddetermining if a change in static factors has occurred, energy savingsmodule 1110 is configured to calculate the energy savings or costassociated with the change, and reporting module 1112 is configured togenerate a report about the change. Facility monitoring module 230 mayuse energy savings module 1110 and reporting module 1112 to generate areport to provide to system feedback module 234 for user output andanalysis. Energy savings module 1110 may have the same functionality asenergy savings module 1008 of FIG. 10, according to an exemplaryembodiment.

Referring now to FIG. 12, process 1200 is used to find a critical valuefor a maximum of a sequence of standardized F-statistics (Z_(Δβ)statistics) where the sequence is determined using the process describedin the present disclosure. Process 1200, according to an exemplaryembodiment, converts the problem of finding this critical value to aproblem of finding a critical value for a maximum element of amultivariate normal vector with a known covariance matrix. In anexemplary embodiment, this problem is solved by performing a Monte Carlosimulation to find the inverse cumulative distribution function (CDF).Process 1200, therefore, evaluates an inverse of a CDF to ultimately usethe values to reject or accept a null hypothesis of an accurate andconstant baseline model, according to an exemplary embodiment.

In multiple hypothesis tests, correlation of the test statistics maylargely impact the conservativeness of typical methods for suppressingthe family-wise probability of falsely rejecting the null hypothesis(Bonferroni's method, for example). For example, if multiple statisticstwo data sets are highly correlated, the statistics do not differ by asignificant amount. Thus, direct applications of Bonferroni's methodwould be very conservative and greatly reduce the power of the test(probability of correctly identifying a change in the modelcoefficients). In the embodiments of the present disclosure, if staticfactors are not changing, the statistics calculated using the windowingmethod described previously should be highly correlated. Window dataselection steps described above could be designed to maintain this highcorrelation during normal behavior. For example, during the reportingperiod, the last data point inserted into the second data windowreplaces the oldest data point in the first data window, meaning thatonly two data points have changed since the last calculation. Using theinverse CDF process of FIG. 12, values that are generated are suitablefor use for determining the accuracy and consistency of a baseline modelwithout having the issue of highly correlated data skewing the results.This is done by determining the autocorrelation of the test statistics,which is dependent on the method used to window the data and to slidethe window forward, and using the correlation to directly calculate (orapproximate) the inverse CDF of the maximum of this autocorrelatedsequence.

Evaluation of the inverse CDF can be phrased as, given a value p (e.g.,a desired probability), find a value x such that P(X<x)=p, where X is arandom variable, in the current disclosure the maximum of the sequenceof statistics and x is the argument of the CDF, which in the currentdisclosure corresponds with the critical value of the null hypothesis(e.g., the value of step 708 of process 700 for the null hypothesistest). In context of the present disclosure, this means the inverse CDFis used to determine a critical value such that the probability that themaximum of the sequence of statistics is equal to the desiredprobability p typically equal to one minus the indicated probability offalsely rejecting the null hypothesis.

If several samples from drawn from the distribution of data points, apoint estimate for the probability p is given by:

${\hat{P}( {X < x} )} = {\hat{p} = \frac{n_{\{{X < x}\}}}{n}}$with an associated 1−a confidence interval for p given by the equationin step 1210 below. The confidence interval 1−a indicates a desiredprobability that the true value of p resides within the band {circumflexover (p)} plus or minus the tolerance.

Process 1200 includes determining a desired probability p (e.g., the pvalue of P(X<x)=p) and a confidence interval for p (step 1202). Thedesired probability p and confidence interval may be chosen by a user ofthe building. The confidence interval should be determined such thatprobabilities with values on the upper and lower limits of the intervalare accepted at the 1−a confidence level. Process 1200 is configured toreturn a value such that all probabilities within the 1−a confidenceinterval are included in the range defined by the upper and lowerlimits. This guarantees that the probability that the actual value of pfor the returned value is between the upper and lower limits is greaterthan 1−a.

Process 1200 further includes determining the number of samples requiredto draw from the distribution in order to reach a desired tolerance(step 1204). The number of samples n may be found by using an iterativeroot finding technique where the objective is to find n such that:

${{\max( {{\frac{n\;\hat{p}\;{F_{{2n\;\hat{p}},{2{\lbrack{{n{({1 - \hat{p}})}} + 1}\rbrack}}}^{- 1}( {a/2} )}}{{n( {1 - \hat{p}} )} + 1 + {n\;\hat{p}\;{F_{{2\; n\hat{p}},{2{\lbrack{{n{({1 - \hat{p}})}} + 1}\rbrack}}}^{- 1}( {a/2} )}}} - {{low}\mspace{14mu}{limit}}},{{{high}\mspace{14mu}{limit}} - \frac{( {{n\;\hat{p}} + 1} ){F_{{2{\lbrack{{n\;\hat{p}} + 1}\rbrack}},{2{n{({1 - \hat{p}})}}}}^{- 1}( {1 - {a/2}} )}}{{n( {1 - \hat{p}} )} + {( {{n\;\hat{p}} + 1} ){F_{{2{\lbrack{{n\;\hat{p}} + 1}\rbrack}},{2{n{({1 - \hat{p}})}}}}^{- 1}( {1 - {a/2}} )}}}}} )} = 0},$where {circumflex over (p)} is given the value of p and the low limitand high limit are the upper and lower limits of the 1−a confidenceinterval.

Process 1200 further includes drawing n samples of the distribution atrandom (step 1206). The samples can be drawn by simulating a linearmodel and performing the process or in order to do an approximation froma multivariate normal distribution. Using the samples, a critical valuex is found such that the total number of samples n drawn less than x isequal to np (e.g., the number of samples times the probability of eachindividual sample being less than x) and the total number of samplesgreater than x is equal to n(1−p) (e.g., the number of samples times theprobability of each individual sample being greater than x) (step 1208).

Process 1200 further includes recalculating the 1−a confidence intervalfor p (step 1210). The equation used for the calculation may be thefollowing:

${P( {\frac{n\;\hat{p}\;{F_{{2\; n\;\hat{p}},{2{\lbrack{{n{({1 - \hat{p}})}} + 1}\rbrack}}}^{- 1}( {a/2} )}}{{n( {1 - \hat{p}} )} + 1 + {n\;\hat{p}\;{F_{{2n\;\hat{p}},{2{\lbrack{{n{({1 - \hat{p}})}} + 1}\rbrack}}}^{- 1}( {a/2} )}}} < p < \frac{( {{n\;\hat{p}} + 1} ){F_{{2{\lbrack{{n\;\hat{p}} + 1}\rbrack}},{2{n{({1 - \hat{p}})}}}}^{- 1}( {1 - {a/2}} )}}{{n( {1 - \hat{p}} )} + {( {{n\;\hat{p}} + 1} ){F_{{2{\lbrack{{n\;\hat{p}} + 1}\rbrack}},{2{n{({1 - \hat{p}})}}}}^{- 1}( {1 - {a/2}} )}}}} )} = {1 - {a.}}$

Process 1200 further includes returning the critical value x and theconfidence interval 1−a for p (step 1212). The critical value is foundby taking the smallest value that will result in a fraction of samplesless than x to be greater than p. x is used for static factor changedetection by process 900 of FIG. 9 and static factor change module 1100of FIG. 11. For example, x is used as the critical value in step 902 ofprocess 900. The difference calculated through each iteration of steps906-908 is compared to the value of x in step 910.

Referring now to FIG. 13, a process 1300 of determining a P-value isshown, according to an exemplary embodiment. Process 1300 may be used todetermine the P-value to use during baseline period detection of staticfactor changes (e.g., step 814 of FIG. 8A). In process 800 of FIG. 8A,since the P-value is not known, an approximate P-value is found usingprocess 1300, and it is determined if the approximate P-value issuitable for use in process 800. Compared to process 1200 for finding acritical value given a known P-value, process 1300 includes having amaximum of a statistic and determining the P-value instead.

Process 1300 includes determining an allowable tolerance on theapproximation of the P-value (step 1302). The allowable tolerance may beset by a user of the building management system or may be determined byprocess 800 or another system of the building management system. Theallowable tolerance relates to a confidence interval or region for whichthe P-value should fall under. For example, the set allowable tolerancemay be compared to the width of the calculated confidence interval (seestep 1308).

Process 1300 includes drawing a number of samples N of the distribution(e.g., data from the two data windows) at random (step 1304). The numberof samples taken may be determined by approximation by taking themaximum of a normal random vector with appropriate correlations or bysimulation of the actual process of calculating the maximum statistic.

Using the samples N, an approximate P-value is calculated as well as aconfidence region or interval for the P-value (step 1306). The P-valueis calculated using the method of step 814 of FIG. 8A, according to anexemplary embodiment. The confidence region for the approximate P-valueis also determined in step 1306. The confidence region is a region forwhich the probability of the actual P-value being within the confidenceregion is greater than a specified threshold. The confidence region is afunction of both the number of samples used in the Monte Carlosimulation (e.g., determined in step 1304) and the P-value (determinedin step 1306). For example, for a given P-value P′ and confidenceinterval for the P-value of [P′−a, P′+b] (where a and b are chosen orcalculated based on the desired threshold) the probability that theactual P-value falls within the confidence interval should be equal to aspecified threshold (e.g., 95%).

Process 1300 further includes comparing the width of the confidenceregion to the tolerance determined in step 1302 (step 1308). If theconfidence region is smaller than the tolerance, then the approximateP-value for which the confidence region is based on is accepted and theP-value is returned to, for example, process 800 for determining achange in static factors (step 1310). If the confidence region isgreater than the tolerance, then the approximate P-value is rejectedsince the approximation of the P-value is not reliable or close enough.Process 1300 then draws another N samples for calculating a newapproximate P-value (step 1304). Step 1308 of comparing the confidenceregion to the tolerance can be described as determining if enoughsamples N were used to approximate the P-value. For example, as moresamples are used to approximate the P-value, the confidence region isreduced as the approximation is more accurate due to the use ofadditional samples.

One embodiment of the present disclosure relates to a method forcalculating a P-value associated with a baseline period test statistic(e.g., a Z-statistic related to a difference in model coefficients). Themethod includes calculating an approximate P-value. The approximateP-value may be calculated using a number of random samples of thedistribution of data used to calculate the model coefficients. Themethod further includes calculating a confidence interval for theP-value and determining the accuracy of the approximate P-value usingthe confidence interval. The method further includes estimating that theapproximate P-value is a reliable P-value when the approximate P-valueis within the confidence interval. The approximate P-value may then beused further steps and calculations (e.g., to determine energy usage andenergy usage changes). If the approximate P-value is not within theconfidence interval during the estimating step, then a new approximateP-value is calculated. The new approximate P-value may be calculated byselecting new samples of the distribution of data used to calculate themodel coefficients.

One embodiment of the present disclosure includes a method forcalculating a critical value associated with a known distribution ofdata points. The critical value is calculated such that the probabilitythat any given test statistic is greater than the critical value matchesa predefined probability of falsely determining that a model has changedin a significant way. The method includes determining a desiredprobability (e.g., user-selected, preselected). The method furtherincludes simulating (e.g., using a Monte Carlo simulation), for varyingsamples of the known distribution of data relevant data points, aprocess of finding the test statistic. The results of the simulations,the desired probability, and a desired confidence interval are used tocalculate the critical value. The method further includes using thecritical value and confidence interval in a comparison against a teststatistic to determine if the model has changed.

While many of the embodiments of the present disclosure relate tobaseline models for energy use, measurement and verification featuresprovided by the above systems and methods may be performed on manybuilding resources or energy types. For example, the measurement andverification as described in the present disclosure may be applied toresource consumption types including electrical energy (or power/demand)usage, natural gas usage, steam usage, water usage, and other types ofresource usage or consumption. As one example, gas and steam consumptioncan be used in the energy usage models described herein. As anotherexample, water usage or consumption (and/or other types of resourceconsumption by a building) may be represented and processed using, e.g.,the baseline models and varying related systems and methods of thepresent disclosure.

Configurations of Various Exemplary Embodiments

The construction and arrangement of the systems and methods as shown inthe various exemplary embodiments are illustrative only. Although only afew embodiments have been described in detail in this disclosure, manymodifications are possible (e.g., variations in sizes, dimensions,structures, shapes and proportions of the various elements, values ofparameters, mounting arrangements, use of materials, colors,orientations, etc.). For example, the position of elements may bereversed or otherwise varied and the nature or number of discreteelements or positions may be altered or varied. Accordingly, all suchmodifications are intended to be included within the scope of thepresent disclosure. The order or sequence of any process or method stepsmay be varied or re-sequenced according to alternative embodiments.Other substitutions, modifications, changes, and omissions may be madein the design, operating conditions and arrangement of the exemplaryembodiments without departing from the scope of the present disclosure.

The present disclosure contemplates methods, systems and programproducts on any machine-readable media for accomplishing variousoperations. The embodiments of the present disclosure may be implementedusing existing computer processors, or by a special purpose computerprocessor for an appropriate system, incorporated for this or anotherpurpose, or by a hardwired system. Embodiments within the scope of thepresent disclosure include program products comprising machine-readablemedia for carrying or having machine-executable instructions or datastructures stored thereon. Such machine-readable media can be anyavailable media that can be accessed by a general purpose or specialpurpose computer or other machine with a processor. By way of example,such machine-readable media can include RAM, ROM, EPROM, EEPROM, CD-ROMor other optical disk storage, magnetic disk storage or other magneticstorage devices, or any other medium which can be used to carry or storedesired program code in the form of machine-executable instructions ordata structures and which can be accessed by a general purpose orspecial purpose computer or other machine with a processor. Combinationsof the above are also included within the scope of machine-readablemedia. Machine-executable instructions include, for example,instructions and data which cause a general purpose computer, specialpurpose computer, or special purpose processing machines to perform acertain function or group of functions.

Although the figures may show a specific order of method steps, theorder of the steps may differ from what is depicted. Also two or moresteps may be performed concurrently or with partial concurrence. Suchvariation will depend on the software and hardware systems chosen and ondesigner choice. All such variations are within the scope of thedisclosure. Likewise, software implementations could be accomplishedwith standard programming techniques with rule based logic and otherlogic to accomplish the various connection steps, processing steps,comparison steps and decision steps.

What is claimed is:
 1. A building management system comprising: aplurality of sensors measuring a physical state or condition of abuilding and generating sensor data indicating the physical state orcondition measured by the plurality of sensors; and a controller thatuses first sensor data from the plurality of sensors collected during afirst time period to generate one or more first model coefficients foran energy usage model for the building; wherein the controller usessecond sensor data from the plurality of sensors collected during asecond time period to generate one or more second model coefficients forthe energy usage model; wherein the controller calculates a differencebetween the first model coefficients and the second model coefficients,generates a test statistic based on the difference, and compares thetest statistic with a critical value; wherein the controllerautomatically identifies a change in the building's energy usage andupdates the energy usage model by replacing the first model coefficientswith the second model coefficients in response to the test statisticexceeding the critical value; wherein the controller uses the energyusage model with the second model coefficients to generate and transmitcontrol signals to building equipment that operate to affect thephysical state or condition of the building in response to replacing thefirst model coefficients with the second model coefficients.
 2. Thebuilding management system of claim 1, wherein: the first and secondmodel coefficients are regression coefficients; and the controllergenerates the first and second model coefficients by performingregression processes using the first and second sensor data.
 3. Thebuilding management system of claim 1, wherein the test statisticrepresents at least one of: the difference between the first and secondmodel coefficients; an F-statistic that is related to the differencebetween the first and second model coefficients; and a Z-statistic thatis formed by standardizing the F-statistic.
 4. The building managementsystem of claim 1, wherein the controller generates a different set ofmodel coefficients for each of a plurality of windows of time,calculates a test statistic for each different set of modelcoefficients, determines an autocorrelation of a plurality of the teststatistics for the plurality of windows of time, and uses theautocorrelation to determine whether the energy usage model with eachdifferent set of model coefficients has changed in a way that affectsenergy usage.
 5. The building management system of claim 1, wherein thefirst time period is prior to implementing an energy conservationmeasure and the second time period is after implementing the energyconservation measure.
 6. The building management system of claim 1,wherein the controller: determines an additional cost or savingsattributable to the identified change in the building's energy usagemodel; and uses the additional cost or savings to adjust the energyusage model with the second model coefficients to provide an accuratecalculation of savings attributable to an energy conservation measure.7. A building management system comprising: a controller that uses firstdata from the building management system collected during a first timeperiod to generate a first energy usage model for a building; whereinthe controller uses second data from the building management systemcollected during a second time period to generate a second energy usagemodel for the building; wherein the controller compares coefficients ofthe first energy usage model with coefficients of the second energyusage model, calculates a difference between the coefficients of thefirst energy usage model and the coefficients of the second energy usagemodel, and determines that a change affecting the building's energyusage has occurred and replaces the first energy usage model with thesecond energy usage model based on the difference; wherein thecontroller uses the second energy usage model to generate and transmitcontrol signals to building equipment that operate to affect a physicalstate or condition of the building in response to replacing the firstenergy usage model with the second energy usage model.
 8. The buildingmanagement system of claim 7, wherein: the coefficients of the first andsecond energy usage models are regression coefficients; and thecontroller generates the coefficients of the first and second energyusage models by performing regression processes using the first andsecond data from the building management system.
 9. The buildingmanagement system of claim 7, wherein the controller: gathers third datafrom the building management system in response to a determination thata change affecting the building's energy usage has occurred; and usesthe third data gathered after the change has occurred to generate athird energy usage model for the building.
 10. The building managementsystem of claim 9, wherein the controller uses the third energy usagemodel to generate and transmit updated control signals for the buildingequipment.
 11. The building management system of claim 7, whereincomparing coefficients of the first and second energy usage modelscomprises generating a test statistic representing at least one of: adifference between the coefficients of the first energy usage model andthe coefficients of the second energy usage model; an F-statistic thatis related to the difference between the coefficients of the firstenergy usage model and the coefficients of the second energy usagemodel; and a Z-statistic that is formed by standardizing theF-statistic.
 12. The building management system of claim 11, wherein thecontroller determines an autocorrelation of a plurality of teststatistics for varying windows of time and uses the autocorrelation todetermine whether a change that affects energy usage has occurred. 13.The building management system of claim 7, wherein the first time periodis prior to implementing an energy conservation measure and the secondtime period is after implementing the energy conservation measure. 14.The building management system of claim 7, wherein the controller:determines an additional cost or savings attributable to the identifiedchange in the building's first or second energy usage model; and usesthe additional cost or savings to adjust at least one of the firstenergy usage model and the second energy usage model to provide anaccurate calculation of savings attributable to an energy conservationmeasure.
 15. A building management system comprising: a controller thatuses first data from the building management system collected during afirst time period to generate a first resource consumption model for abuilding; wherein the controller uses second data from the buildingmanagement system collected during a second time period to generate asecond resource consumption model for the building; wherein thecontroller compares coefficients of the first resource consumption modelwith coefficients of the second resource consumption model, calculates adifference between the coefficients of the first resource consumptionmodel and the coefficients of the second resource consumption model, anddetermines that a change affecting the building's resource consumptionhas occurred and replaces the first resource consumption model with thesecond resource consumption model based on the difference; wherein thecontroller uses the second resource consumption model to generate andtransmit control signals to building equipment that operate to affect aphysical state or condition of the building in response to replacing thefirst resource consumption model with the second resource consumptionmodel.
 16. The building management system of claim 15, wherein theresource consumption model corresponds to at least one of water usage,gas usage, and electrical energy usage.
 17. The building managementsystem of claim 15, wherein: the coefficients of the first and secondresource consumption models are regression coefficients; and thecontroller generates the coefficients of the first and second resourceconsumption models by performing regression processes using the firstand second data from the building management system.
 18. The buildingmanagement system of claim 15, wherein the controller: gathers thirddata from the building management system in response to a determinationthat a change affecting the building's resource consumption hasoccurred; and uses the third data gathered after the change has occurredto generate a third resource consumption model for the building.
 19. Thebuilding management system of claim 18, wherein the controller uses thethird resource consumption model to generate updated control signals forthe building equipment.
 20. The building management system of claim 15,wherein comparing coefficients of the first and second resourceconsumption models comprises generating a test statistic representing atleast one of: a difference between the coefficients of the firstresource consumption model and the coefficients of the second resourceconsumption model; an F-statistic that is related to the differencebetween the coefficients of the first resource consumption model and thecoefficients of the second resource consumption model; and a Z-statisticthat is formed by standardizing the F-statistic.